Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

As transport networks become more congested, there is a growing need to adopt policies that manage demand and make full use of existing assets. Advances in information technology are now such that intelligent transportation systems (ITS) offer real potential to meet this challenge by monitoring current conditions, predicting what might happen in the future, and providing the means to manage transport proactively and on an area-wide basis. Modeling and Simulation of Intelligent Transportation Systems provides engineers, professionals, and researchers an intuitive appreciation for ITS theory, related sensor technologies, and other practical applications, including traffic management, safety, design optimization, and sustainability. Provides the theory and practical applications of Intelligent Transport Theory which will be helpful as highway construction recedes as a sustainable long-term solution. Includes several case studies that illustrate the concepts presented throughout.

Life-Cycle of Structures and Infrastructure Systems contains the lectures and papers presented at IALCCE 2023- The Eighth International Symposium on Life-Cycle Civil Engineering, held at Politecnico di Milano, Milan, Italy, 2-6 July, 2023. This book contains the full papers of 514 contributions presented at IALCCE 2023, including the Fazlur R. Khan Plenary Lecture, nine Keynote Lectures, and 504 technical papers from 45 countries. The papers cover recent advances and cutting-edge research in the field of life-cycle civil engineering, including emerging concepts and innovative applications related to life-cycle design, assessment, inspection, monitoring, repair, maintenance, rehabilitation, and management of structures and infrastructure systems under uncertainty. Major topics covered include life-cycle safety, reliability, risk, resilience and sustainability, life-cycle damaging processes, life-cycle design and assessment, life-cycle inspection and monitoring, life-cycle maintenance and management, life-cycle performance of special structures, life-cycle cost of structures and infrastructure systems, and life-cycle-oriented computational tools, among others. This Open Access Book provides both an up-to-date overview of the field of life-cycle civil engineering and significant contributions to the process of making more rational decisions to mitigate the life-cycle risk and improve the life-cycle reliability, resilience, and sustainability of structures and infrastructure systems exposed to multiple natural and human-made hazards in a changing climate. It will serve as a valuable reference to all concerned with life-cycle of civil engineering systems, including students, researchers, practicioners, consultants, contractors, decision makers, and representatives of managing bodies and public authorities from all branches of civil engineering.

This volume gathers the latest advances and innovations in the field of structural health monitoring, as presented at the 8th Civil Structural Health Monitoring Workshop (CSHM-8), held on March 31–April 2, 2021. It discusses emerging challenges in civil SHM and more broadly in the fields of smart materials and intelligent systems for civil engineering applications. The contributions cover a diverse range of topics, including applications of SHM to civil structures and infrastructures, innovative sensing solutions for SHM, data-driven damage detection techniques, nonlinear systems and analysis techniques, influence of environmental and operational conditions, aging structures and infrastructures in hazardous environments, and SHM in earthquake prone regions. Selected by means of a rigorous peer-review process, they will spur novel research directions and foster future multidisciplinary collaborations.

Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.

A critical review of key developments and latest advances in Structural Health Monitoring technologies applied to civil engineering structures, covering all aspects required for practical application Structural Health Monitoring (SHM) provides the facilities for in-service monitoring of structural performance and damage assessment, and is a key element of condition based maintenance and damage prognosis. This comprehensive book brings readers up to date on the most important changes and advancements in the structural health monitoring technologies applied to civil engineering structures. It covers all aspects required for such monitoring in the field, including sensors and networks, data acquisition and processing, damage detection techniques and damage prognostics techniques. The book also includes a number of case studies showing how the techniques can be applied in the development of sustainable and resilient civil infrastructure systems. Structural Health Monitoring of Large Civil Engineering Structures offers in-depth chapter coverage of: Sensors and Sensing Technology for Structural Monitoring; Data Acquisition, Transmission, and Management; Structural Damage Identification Techniques; Modal Analysis of Civil Engineering Structures; Finite Element Model Updating; Vibration Based Damage Identification Methods; Model Based Damage Assessment Methods; Monitoring Based Reliability Analysis and Damage Prognosis; and Applications of SHM Strategies to Large Civil Structures. Presents state-of-the-art SHM technologies allowing asset managers to evaluate structural performance and make rational decisions Covers all aspects required for the practical application of SHM Includes case studies that show how the techniques can be applied in practice Structural Health Monitoring of Large Civil Engineering Structures is an ideal book for practicing civil engineers, academics and postgraduate students studying civil and structural engineering.

This book is a printed edition of the Special Issue "Structural Health Monitoring (SHM) of Civil Structures" that was published in Applied Sciences

The use of fibre optic sensors in structural health monitoring has rapidly accelerated in recent years. By embedding fibre optic sensors in structures (e.g. buildings, bridges and pipelines) it is possible to obtain real time data on structural changes such as stress or strain. Engineers use monitoring data to detect deviations from a structure’s original design performance in order to optimise the operation, repair and maintenance of a structure over time. Fibre Optic Methods for Structural Health Monitoring is organised as a step-by-step guide to implementing a monitoring system and includes examples of common structures and their most-frequently monitored parameters. This book: presents a universal method for static structural health monitoring, using a technique with proven effectiveness in hundreds of applications worldwide; discusses a variety of different structures including buildings, bridges, dams, tunnels and pipelines; features case studies which describe common problems and offer solutions to those problems; provides advice on establishing mechanical parameters to monitor (including deformations, rotations and displacements) and on placing sensors to achieve monitoring objectives; identifies methods for interpreting data according to construction material and shows how to apply numerical concepts and formulae to data in order to inform decision making. Fibre Optic Methods for Structural Health Monitoring is an invaluable reference for practising engineers in the fields of civil, structural and geotechnical engineering. It will also be of interest to academics and undergraduate/graduate students studying civil and structural engineering.